MCP Servers

MCP server that provides access to Wikipedia content. Lets AI agents search articles, fetch full or summarized page content, and resolve references, giving models reliable, citable background knowledge for research and question answering.

Provides persistent memory capabilities through a knowledge graph stored in a local JSON file. Enables AI agents to create, read, update, and delete entities and their relationships. Useful for maintaining context across conversations, storing user preferences, and building structured knowledge bases that persist between sessions.

Official Notion MCP server for workspace integration. Enables AI agents to search pages, read content, create new pages, update existing pages, and manage databases in Notion. Supports rich text content, database queries with filters, and page property management for seamless knowledge base interaction from AI-powered tools.

MCP server for Firecrawl's web scraping and crawling API. Converts any website into clean, LLM-ready markdown or structured data. Supports single page scraping, multi-page crawling with depth control, sitemap-based extraction, and batch operations. Handles JavaScript-rendered pages, bypasses common anti-bot measures, and returns structured content suitable for RAG pipelines and knowledge base construction.

MCP server for Atlassian Confluence that lets AI agents search the knowledge base, read and create pages, update content, and navigate spaces. Useful for grounding answers in internal documentation and for drafting or maintaining wiki content directly from an AI client.

Connects AI agents to ChromaDB vector databases for semantic search and retrieval augmented generation (RAG) workflows. Supports creating collections, upserting documents with embeddings, querying by semantic similarity, and managing metadata filters. Ideal for knowledge base and document retrieval applications.

Official MCP server for Qdrant vector search engine. Acts as a semantic memory layer enabling AI agents to store and retrieve information using vector similarity search. Supports storing text with metadata, semantic querying, configurable embedding models via FastEmbed, and both cloud-hosted and local Qdrant instances. Useful for building RAG pipelines, code search, knowledge bases, and long-term agent memory.

MCP server for Airtable that lets AI agents read and write records, list bases and tables, inspect field schemas, and run filtered queries. Ideal for turning Airtable into a lightweight backend or knowledge base that agents can manage conversationally.

Official PagerDuty MCP server for incident management. Lets AI agents list and triage incidents, acknowledge and resolve them, look up on-call schedules, and query services so responders can manage operational incidents from AI-powered tools.